Our scoring-based classifier borrows the idea of HolE [1]}, and theoretically, it should also be able to learn the correlation and mutual exclusion between relations.
To verify the learning ability of our classifier, we visualize the relation representations of NYT by the \(t-\) SNE [2]}, which is a nonlinear dimensionality reduction algorithm. We omit 6 long-tail relations that appear less than 50 times in whole training set, and the results are shown in Figure REF .
It can be observed that
the topology structure of relations reflects their inherent connections.
For instance, the relations related to people are on the left region of the figure, while the relations related to location are on the right.
Especially, the spatial positions of place_lived, place_of_birth and place_of_death are very close, which is in consistent with our common sense.
This property is critical for predicting EPO triples. That is, if the model predicts the relation place_of_birth for an entity pair, we can infer that the entity pair may hold another relation place_lived with a high-probability.
These all suggest that our method has learned not only the features of a specific dataset, but also the general knowledge that conforms to the real world.
Thus, our OneRel may have generalization capabilities.
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